Physical activity recommendations in patients with chronic obstructive pulmonary disease.

BACKGROUND: Physical activity recommendations are hardly studied in patients with chronic obstructive pulmonary disease (COPD), and specifically recommendations that are individualized to a patient's aerobic fitness level are not studied. OBJECTIVES: To compare individualized (relative) and nonindividualized (absolute) physical activity recommendations in patients with COPD and to assess whether there are differences between patients with mild to moderate and (very) severe COPD. METHODS: We compared 7 different physical activity recommendations that were described in the literature. Four recommendations were individualized based on the patient's aerobic fitness level measured by a maximal cycle ergometer test. Three recommendations were nonindividualized. The recommendations were measured with an accelerometer, pedometer or questionnaire in 115 patients with mild to very severe COPD (68% male, mean age 65 years, mean FEV1 58% predicted). RESULTS: The percentage of patients that met the different recommendations ranged from 22 to 86% and only 8 patients met all 7 recommendations. The agreement between the different recommendations was poor (intraclass correlation coefficient, 0.28). Individualizing the recommendations resulted in a higher number of patients with severe or very severe COPD meeting the individualized recommendations compared to the nonindividualized recommendations. In contrast, patients with mild to moderate COPD less frequently met the individualized recommendations. CONCLUSIONS: Our study showed that applying various physical activity recommendations with small differences in frequency, intensity or time led to large differences in the classification of patients with COPD into being sufficiently physically active or not. Consequently, the used recommendation will highly affect the proposed physical activity advice to the patient.

The association between objectively measured physical activity, depression, cognition, and health-related quality of life in Parkinson's disease.

BACKGROUND: Lower levels of physical activity are associated with lower Health-Related Quality of Life (HRQoL) in Parkinson's disease (PD). We evaluated the influence of quantitative physical activity parameters among other (disease-related) features representing other domains of the WHO International model for classification of Function, Disability, and Health (ICF) on HRQoL in PD. METHODS: Home-based movement data (DynaPort MiniMod®) was collected in 47 PD patients. Nine stepwise regression models were calculated, with consecutive outcome variables: Parkinson's Disease Questionnaire (PDQ) Summary Index (SI), PDQ-Mobility, PDQ-Activities of Daily Living (ADL). Demographic variables, disease-specific features, and quantitative physical activity parameters, were included as predicting variables in all analyses. The following three physical activity parameters were alternately included for both sedentary and active episodes: 'percentage' of 24 h spent within these episodes, 'number of bouts', and 'mean bout lengths' (MBL). RESULTS: Depression and 'Total Energy Expenditure' were the main predictors of overall HRQoL (PDQ-SI), independent of the permutation of activity parameters. The same parameters predicted the PDQ-Mobility score. However, this result was altered when 'MBL' parameters were included into the model, 'MBL' of sedentary episodes additionally predicted HRQoL-Mobility. The PDQ-ADL score was associated with demographic, motor, and non-motor variables including cognitive status. After exclusion of demented PD patients, older age and cognitive impairment no longer constrained HRQoL-ADL. DISCUSSION: For the first time, we showed the influence of objective, home-based measured physical activity among depression and cognition on HRQoL in PD. This suggests that a multifactorial treatment approach would be most successful to increase HRQoL in PD.

Stride frequency in relation to oxygen consumption in experienced and novice runners.

We hypothesised that experienced runners would select a stride frequency closer to the optimum (minimal energy costs) than would novice runners. In addition, we expected that optimal stride frequency could simply be determined by monitoring heart rate without measuring oxygen consumption (VO2). Ten healthy males (mean ±s: 24±2 year) with no running training experience and 10 trained runners of similar age ran at constant treadmill speed corresponding to 80% of individual ventilatory threshold. For two days, they ran at seven different stride frequencies (self-selected stride frequency ±18%) imposed by a metronome. Optimal stride frequency was based on the minimum of a second-order polynomial equation fitted through steady state VO2 at each stride frequency. Running cost (mean±s) at optimal stride frequency was higher (P < 0.05) in novice (236±31 ml O2·kg(-1.) km(-1)) than trained (189±13 ml O2·kg(-1.) km(-1)) runners. Self-selected stride frequency (mean ±s; strides(.)min(-1)) for novice (77.8±2.8) and trained runners (84.4±5.3) were lower (P < 0.05) than optimal stride frequency (respectively, 84.9±5.0 and 87.1±4.8). The difference between self-selected and optimal stride frequency was smaller (P < 0.05) for trained runners. In both the groups optimal stride frequency established with heart rate was not different (P > 0.3) from optimal stride frequency based on VO2. In each group and despite limited variation between participants, optimal stride frequencies derived from VO2 and heart rate were related (r > 0.7; P < 0.05). In conclusion, trained runners chose a stride frequency closer to the optimum for energy expenditure than novices. Heart rate could be used to establish optimal stride frequency.